Showing 161 - 180 results of 7,262 for search '(( significantly ((linear decrease) OR (we decrease)) ) OR ( significant shape decrease ))', query time: 0.54s Refine Results
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    The statistical data of the partial graph. by Si Yu Zhao (19544793)

    Published 2024
    “…Behavioral tests of both mutant and control strains revealed that the <i>rho-l</i><sup><i>△807</i></sup> mutant mosquitoes had a significant decrease in their ability to search for preferred oviposition sites that correlated with a reduced ability to recognize long-wavelength red light. …”
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    Experimental Design Flowchart. by Si Yu Zhao (19544793)

    Published 2024
    “…Behavioral tests of both mutant and control strains revealed that the <i>rho-l</i><sup><i>△807</i></sup> mutant mosquitoes had a significant decrease in their ability to search for preferred oviposition sites that correlated with a reduced ability to recognize long-wavelength red light. …”
  7. 167

    Actual measurement of shape errors. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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    Detailed information of the observation datasets. by Weidong Ji (129916)

    Published 2025
    “…Understanding spatial-temporal characteristics of wind speed is significant in meteorology, coastal engineering design and maritime industries. …”
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    General technical specification for GW154/6700. by Weidong Ji (129916)

    Published 2025
    “…Understanding spatial-temporal characteristics of wind speed is significant in meteorology, coastal engineering design and maritime industries. …”
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    Geometric manifold comparison visualization by Eloy Geenjaar (21533195)

    Published 2025
    “…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
  20. 180

    Hyperparameter ranges by Eloy Geenjaar (21533195)

    Published 2025
    “…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”